{"id":"W4362658038","doi":"10.1002/sim.9734","title":"Point estimation for adaptive trial designs <scp>II</scp>: Practical considerations and guidance","year":2023,"lang":"en","type":"review","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR Cambridge Biomedical Research Centre; Medical Research Council; National Institute for Health and Care Research; Medical Research Council Canada; Department of Health and Social Care; Cancer Research UK; Health and Care Research Wales","keywords":"Estimation; Computer science; Point estimation; Point (geometry); Econometrics; Statistics; Mathematical optimization; Mathematics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2512707,0.002838726,0.004692654,0.004532602,0.001412652,0.007906327,0.007316095,0.01746066,0.01563631],"category_scores_gemma":[0.6518013,0.002946939,0.004798477,0.005839299,0.008351244,0.007772148,0.00462376,0.02438579,0.01459033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004656549,"about_ca_system_score_gemma":0.01443978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005753663,"about_ca_topic_score_gemma":0.004508335,"domain_scores_codex":[0.629602,0.31962,0.02104149,0.005237798,0.02358843,0.0009103673],"domain_scores_gemma":[0.2959732,0.6319166,0.01557336,0.02411288,0.03093564,0.001488413],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007189919,0.0001428744,0.0006888867,0.007809886,0.0003491865,0.0003874099,0.001538606,0.01403161,0.0005107025,0.3367481,0.2901253,0.3469485],"study_design_scores_gemma":[0.001233829,0.0006388678,0.001141932,0.015596,0.0002430736,0.0009647739,0.0002054653,0.04646418,0.001346409,0.4974116,0.4343931,0.0003608455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0004635405,0.01602021,0.9120879,0.04557169,0.004070856,0.003560486,0.001130874,0.001772552,0.01532189],"genre_scores_gemma":[0.007780788,0.01010632,0.9464024,0.01778984,0.003644409,0.01031821,0.0005618898,0.0007280165,0.002668129],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.7487293,"threshold_uncertainty_score":0.9233165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8358348090652867,"score_gpt":0.6690181020821543,"score_spread":0.1668167069831324,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}